Sponsio MCP Server
SponsioLabs/Sponsio
[](https://agentrank-ai.com/tool/SponsioLabs--Sponsio/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Deterministic safety solutions for probabilistic AI agents
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
SponsioLabs/Sponsio is a Python tool licensed under Apache-2.0. Deterministic safety solutions for probabilistic AI agents Topics: agent-guardrails, agent-harness, agent-runtime, agent-safety, agent-security, agent-skills, agentic-ai, ai-agents, ai-security, deterministic, guardrails, mcp, open-source, openclaw, openclaw-skills, policy-engine, prompt-injection, runtime-safety, self-hosted, intent-verification.
Ranked #5046 out of 24846 indexed tools.
Ecosystem
Score Breakdown
454 stars → growing community
Last commit 29d ago → recently updated
2/8 issues closed → many open issues
5 contributors → small team
No dependents → no downstream usage
93 weekly installs → early adoption
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
Matched Queries
From the README
<p align="right"> <b>English</b> · <a href="./README.zh-CN.md">简体中文</a> · <a href="./README.ja.md">日本語</a> </p> <p align="center"> <a href="https://opensource.org/licenses/Apache-2.0"></a> <a href="https://pypi.org/project/sponsio/"></a> <a href="https://sponsio.dev"></a> </p> <p align="center"> <a href="https://x.com/sponsiolabs"></a> <a href="https://www.linkedin.com/company/sponsio-labs/"></a> <a href="https://discord.gg/s8TfPnZWUm"></a> </p> # Sponsio <p align="center"> </p> Sponsio checks an agent's tool calls before they run. A rule can look at what already happened, so "check the policy before issuing a refund" is one rule instead of a paragraph of prompt. Each check takes under 0.01 ms and calls no model. Works with LangChain, Claude Agent, OpenAI Agents, Google ADK, CrewAI, Vercel AI, MCP, or any custom tool-calling loop, in Python or TypeScript. > An **agent contract** is a runtime rule that is checked at every agent action, [backed by formal methodsRead full README on GitHub →
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